--- _id: '15020' abstract: - lang: eng text: "This thesis consists of four distinct pieces of work within theoretical biology, with two themes in common: the concept of optimization in biological systems, and the use of information-theoretic tools to quantify biological stochasticity and statistical uncertainty.\r\nChapter 2 develops a statistical framework for studying biological systems which we believe to be optimized for a particular utility function, such as retinal neurons conveying information about visual stimuli. We formalize such beliefs as maximum-entropy Bayesian priors, constrained by the expected utility. We explore how such priors aid inference of system parameters with limited data and enable optimality hypothesis testing: is the utility higher than by chance?\r\nChapter 3 examines the ultimate biological optimization process: evolution by natural selection. As some individuals survive and reproduce more successfully than others, populations evolve towards fitter genotypes and phenotypes. We formalize this as accumulation of genetic information, and use population genetics theory to study how much such information can be accumulated per generation and maintained in the face of random mutation and genetic drift. We identify the population size and fitness variance as the key quantities that control information accumulation and maintenance.\r\nChapter 4 reuses the concept of genetic information from Chapter 3, but from a different perspective: we ask how much genetic information organisms actually need, in particular in the context of gene regulation. For example, how much information is needed to bind transcription factors at correct locations within the genome? Population genetics provides us with a refined answer: with an increasing population size, populations achieve higher fitness by maintaining more genetic information. Moreover, regulatory parameters experience selection pressure to optimize the fitness-information trade-off, i.e. minimize the information needed for a given fitness. This provides an evolutionary derivation of the optimization priors introduced in Chapter 2.\r\nChapter 5 proves an upper bound on mutual information between a signal and a communication channel output (such as neural activity). Mutual information is an important utility measure for biological systems, but its practical use can be difficult due to the large dimensionality of many biological channels. Sometimes, a lower bound on mutual information is computed by replacing the high-dimensional channel outputs with decodes (signal estimates). Our result provides a corresponding upper bound, provided that the decodes are the maximum posterior estimates of the signal." acknowledged_ssus: - _id: ScienComp alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Michal full_name: Hledik, Michal id: 4171253A-F248-11E8-B48F-1D18A9856A87 last_name: Hledik citation: ama: Hledik M. Genetic information and biological optimization. 2024. doi:10.15479/at:ista:15020 apa: Hledik, M. (2024). Genetic information and biological optimization. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:15020 chicago: Hledik, Michal. “Genetic Information and Biological Optimization.” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:15020. ieee: M. Hledik, “Genetic information and biological optimization,” Institute of Science and Technology Austria, 2024. ista: Hledik M. 2024. Genetic information and biological optimization. Institute of Science and Technology Austria. mla: Hledik, Michal. Genetic Information and Biological Optimization. Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:15020. short: M. Hledik, Genetic Information and Biological Optimization, Institute of Science and Technology Austria, 2024. date_created: 2024-02-23T14:02:04Z date_published: 2024-02-23T00:00:00Z date_updated: 2024-03-06T14:22:52Z day: '23' ddc: - '576' - '519' degree_awarded: PhD department: - _id: GradSch - _id: NiBa - _id: GaTk doi: 10.15479/at:ista:15020 ec_funded: 1 file: - access_level: open_access checksum: b2d3da47c98d481577a4baf68944fe41 content_type: application/pdf creator: mhledik date_created: 2024-02-23T13:50:53Z date_updated: 2024-02-23T13:50:53Z file_id: '15021' file_name: hledik thesis pdfa 2b.pdf file_size: 7102089 relation: main_file success: 1 - access_level: closed checksum: eda9b9430da2610fee7ce1c1419a479a content_type: application/zip creator: mhledik date_created: 2024-02-23T13:50:54Z date_updated: 2024-02-23T14:20:16Z file_id: '15022' file_name: hledik thesis source.zip file_size: 14014790 relation: source_file file_date_updated: 2024-02-23T14:20:16Z has_accepted_license: '1' keyword: - Theoretical biology - Optimality - Evolution - Information language: - iso: eng month: '02' oa: 1 oa_version: Published Version page: '158' project: - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program - _id: 2665AAFE-B435-11E9-9278-68D0E5697425 grant_number: RGP0034/2018 name: Can evolution minimize spurious signaling crosstalk to reach optimal performance? - _id: bd6958e0-d553-11ed-ba76-86eba6a76c00 grant_number: '101055327' name: Understanding the evolution of continuous genomes publication_identifier: issn: - 2663 - 337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '7553' relation: part_of_dissertation status: public - id: '12081' relation: part_of_dissertation status: public - id: '7606' relation: part_of_dissertation status: public status: public supervisor: - first_name: Nicholas H full_name: Barton, Nicholas H id: 4880FE40-F248-11E8-B48F-1D18A9856A87 last_name: Barton orcid: 0000-0002-8548-5240 - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 title: Genetic information and biological optimization type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2024' ... --- _id: '12081' abstract: - lang: eng text: 'Selection accumulates information in the genome—it guides stochastically evolving populations toward states (genotype frequencies) that would be unlikely under neutrality. This can be quantified as the Kullback–Leibler (KL) divergence between the actual distribution of genotype frequencies and the corresponding neutral distribution. First, we show that this population-level information sets an upper bound on the information at the level of genotype and phenotype, limiting how precisely they can be specified by selection. Next, we study how the accumulation and maintenance of information is limited by the cost of selection, measured as the genetic load or the relative fitness variance, both of which we connect to the control-theoretic KL cost of control. The information accumulation rate is upper bounded by the population size times the cost of selection. This bound is very general, and applies across models (Wright–Fisher, Moran, diffusion) and to arbitrary forms of selection, mutation, and recombination. Finally, the cost of maintaining information depends on how it is encoded: Specifying a single allele out of two is expensive, but one bit encoded among many weakly specified loci (as in a polygenic trait) is cheap.' acknowledgement: We thank Ksenia Khudiakova, Wiktor Młynarski, Sean Stankowski, and two anonymous reviewers for discussions and comments on the manuscript. G.T. and M.H. acknowledge funding from the Human Frontier Science Program Grant RGP0032/2018. N.B. acknowledges funding from ERC Grant 250152 “Information and Evolution.” article_number: e2123152119 article_processing_charge: No article_type: original author: - first_name: Michal full_name: Hledik, Michal id: 4171253A-F248-11E8-B48F-1D18A9856A87 last_name: Hledik - first_name: Nicholas H full_name: Barton, Nicholas H id: 4880FE40-F248-11E8-B48F-1D18A9856A87 last_name: Barton orcid: 0000-0002-8548-5240 - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: '1' citation: ama: Hledik M, Barton NH, Tkačik G. Accumulation and maintenance of information in evolution. Proceedings of the National Academy of Sciences. 2022;119(36). doi:10.1073/pnas.2123152119 apa: Hledik, M., Barton, N. H., & Tkačik, G. (2022). Accumulation and maintenance of information in evolution. Proceedings of the National Academy of Sciences. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.2123152119 chicago: Hledik, Michal, Nicholas H Barton, and Gašper Tkačik. “Accumulation and Maintenance of Information in Evolution.” Proceedings of the National Academy of Sciences. Proceedings of the National Academy of Sciences, 2022. https://doi.org/10.1073/pnas.2123152119. ieee: M. Hledik, N. H. Barton, and G. Tkačik, “Accumulation and maintenance of information in evolution,” Proceedings of the National Academy of Sciences, vol. 119, no. 36. Proceedings of the National Academy of Sciences, 2022. ista: Hledik M, Barton NH, Tkačik G. 2022. Accumulation and maintenance of information in evolution. Proceedings of the National Academy of Sciences. 119(36), e2123152119. mla: Hledik, Michal, et al. “Accumulation and Maintenance of Information in Evolution.” Proceedings of the National Academy of Sciences, vol. 119, no. 36, e2123152119, Proceedings of the National Academy of Sciences, 2022, doi:10.1073/pnas.2123152119. short: M. Hledik, N.H. Barton, G. Tkačik, Proceedings of the National Academy of Sciences 119 (2022). date_created: 2022-09-11T22:01:55Z date_published: 2022-08-29T00:00:00Z date_updated: 2024-03-06T14:22:51Z day: '29' ddc: - '570' department: - _id: NiBa - _id: GaTk doi: 10.1073/pnas.2123152119 ec_funded: 1 external_id: isi: - '000889278400014' pmid: - '36037343' file: - access_level: open_access checksum: 6dec51f6567da9039982a571508a8e4d content_type: application/pdf creator: dernst date_created: 2022-09-12T08:08:12Z date_updated: 2022-09-12T08:08:12Z file_id: '12091' file_name: 2022_PNAS_Hledik.pdf file_size: 2165752 relation: main_file success: 1 file_date_updated: 2022-09-12T08:08:12Z has_accepted_license: '1' intvolume: ' 119' isi: 1 issue: '36' language: - iso: eng month: '08' oa: 1 oa_version: Published Version pmid: 1 project: - _id: 25B07788-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '250152' name: Limits to selection in biology and in evolutionary computation - _id: 2665AAFE-B435-11E9-9278-68D0E5697425 grant_number: RGP0034/2018 name: Can evolution minimize spurious signaling crosstalk to reach optimal performance? publication: Proceedings of the National Academy of Sciences publication_identifier: eissn: - 1091-6490 issn: - 0027-8424 publication_status: published publisher: Proceedings of the National Academy of Sciences quality_controlled: '1' related_material: record: - id: '15020' relation: dissertation_contains status: public scopus_import: '1' status: public title: Accumulation and maintenance of information in evolution tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 119 year: '2022' ... --- _id: '9816' abstract: - lang: eng text: "Aims: Mass antigen testing programs have been challenged because of an alleged insufficient specificity, leading to a large number of false positives. The objective of this study is to derive a lower bound of the specificity of the SD Biosensor Standard Q Ag-Test in large scale practical use.\r\nMethods: Based on county data from the nationwide tests for SARS-CoV-2 in Slovakia between 31.10.–1.11. 2020 we calculate a lower confidence bound for the specificity. As positive test results were not systematically verified by PCR tests, we base the lower bound on a worst case assumption, assuming all positives to be false positives.\r\nResults: 3,625,332 persons from 79 counties were tested. The lowest positivity rate was observed in the county of Rožňava where 100 out of 34307 (0.29%) tests were positive. This implies a test specificity of at least 99.6% (97.5% one-sided lower confidence bound, adjusted for multiplicity).\r\nConclusion: The obtained lower bound suggests a higher specificity compared to earlier studies in spite of the underlying worst case assumption and the application in a mass testing setting. The actual specificity is expected to exceed 99.6% if the prevalence in the respective regions was non-negligible at the time of testing. To our knowledge, this estimate constitutes the first bound obtained from large scale practical use of an antigen test." acknowledgement: We would like to thank Alfred Uhl, Richard Kollár and Katarína Bod’ová for very helpful comments. We also thank Matej Mišík for discussion and information regarding the Slovak testing data and Ag-Test used. article_number: e0255267 article_processing_charge: Yes article_type: original author: - first_name: Michal full_name: Hledik, Michal id: 4171253A-F248-11E8-B48F-1D18A9856A87 last_name: Hledik - first_name: Jitka full_name: Polechova, Jitka id: 3BBFB084-F248-11E8-B48F-1D18A9856A87 last_name: Polechova orcid: 0000-0003-0951-3112 - first_name: Mathias full_name: Beiglböck, Mathias last_name: Beiglböck - first_name: Anna Nele full_name: Herdina, Anna Nele last_name: Herdina - first_name: Robert full_name: Strassl, Robert last_name: Strassl - first_name: Martin full_name: Posch, Martin last_name: Posch citation: ama: Hledik M, Polechova J, Beiglböck M, Herdina AN, Strassl R, Posch M. Analysis of the specificity of a COVID-19 antigen test in the Slovak mass testing program. PLoS ONE. 2021;16(7). doi:10.1371/journal.pone.0255267 apa: Hledik, M., Polechova, J., Beiglböck, M., Herdina, A. N., Strassl, R., & Posch, M. (2021). Analysis of the specificity of a COVID-19 antigen test in the Slovak mass testing program. PLoS ONE. Public Library of Science. https://doi.org/10.1371/journal.pone.0255267 chicago: Hledik, Michal, Jitka Polechova, Mathias Beiglböck, Anna Nele Herdina, Robert Strassl, and Martin Posch. “Analysis of the Specificity of a COVID-19 Antigen Test in the Slovak Mass Testing Program.” PLoS ONE. Public Library of Science, 2021. https://doi.org/10.1371/journal.pone.0255267. ieee: M. Hledik, J. Polechova, M. Beiglböck, A. N. Herdina, R. Strassl, and M. Posch, “Analysis of the specificity of a COVID-19 antigen test in the Slovak mass testing program,” PLoS ONE, vol. 16, no. 7. Public Library of Science, 2021. ista: Hledik M, Polechova J, Beiglböck M, Herdina AN, Strassl R, Posch M. 2021. Analysis of the specificity of a COVID-19 antigen test in the Slovak mass testing program. PLoS ONE. 16(7), e0255267. mla: Hledik, Michal, et al. “Analysis of the Specificity of a COVID-19 Antigen Test in the Slovak Mass Testing Program.” PLoS ONE, vol. 16, no. 7, e0255267, Public Library of Science, 2021, doi:10.1371/journal.pone.0255267. short: M. Hledik, J. Polechova, M. Beiglböck, A.N. Herdina, R. Strassl, M. Posch, PLoS ONE 16 (2021). date_created: 2021-08-08T22:01:26Z date_published: 2021-07-29T00:00:00Z date_updated: 2023-08-10T14:26:32Z day: '29' ddc: - '610' department: - _id: NiBa doi: 10.1371/journal.pone.0255267 external_id: isi: - '000685248200095' pmid: - '34324553' file: - access_level: open_access checksum: ae4df60eb62f4491278588548d0c1f93 content_type: application/pdf creator: asandaue date_created: 2021-08-09T11:52:14Z date_updated: 2021-08-09T11:52:14Z file_id: '9835' file_name: 2021_PLoSONE_Hledík.pdf file_size: 773921 relation: main_file success: 1 file_date_updated: 2021-08-09T11:52:14Z has_accepted_license: '1' intvolume: ' 16' isi: 1 issue: '7' language: - iso: eng month: '07' oa: 1 oa_version: Published Version pmid: 1 publication: PLoS ONE publication_identifier: eissn: - 1932-6203 publication_status: published publisher: Public Library of Science quality_controlled: '1' scopus_import: '1' status: public title: Analysis of the specificity of a COVID-19 antigen test in the Slovak mass testing program tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 16 year: '2021' ... --- _id: '7553' abstract: - lang: eng text: Normative theories and statistical inference provide complementary approaches for the study of biological systems. A normative theory postulates that organisms have adapted to efficiently solve essential tasks, and proceeds to mathematically work out testable consequences of such optimality; parameters that maximize the hypothesized organismal function can be derived ab initio, without reference to experimental data. In contrast, statistical inference focuses on efficient utilization of data to learn model parameters, without reference to any a priori notion of biological function, utility, or fitness. Traditionally, these two approaches were developed independently and applied separately. Here we unify them in a coherent Bayesian framework that embeds a normative theory into a family of maximum-entropy “optimization priors.” This family defines a smooth interpolation between a data-rich inference regime (characteristic of “bottom-up” statistical models), and a data-limited ab inito prediction regime (characteristic of “top-down” normative theory). We demonstrate the applicability of our framework using data from the visual cortex, and argue that the flexibility it affords is essential to address a number of fundamental challenges relating to inference and prediction in complex, high-dimensional biological problems. acknowledgement: The authors thank Dario Ringach for providing the V1 receptive fields and Olivier Marre for providing the retinal receptive fields. W.M. was funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 754411. M.H. was funded in part by Human Frontiers Science grant no. HFSP RGP0032/2018. article_processing_charge: No author: - first_name: Wiktor F full_name: Mlynarski, Wiktor F id: 358A453A-F248-11E8-B48F-1D18A9856A87 last_name: Mlynarski - first_name: Michal full_name: Hledik, Michal id: 4171253A-F248-11E8-B48F-1D18A9856A87 last_name: Hledik - first_name: Thomas R full_name: Sokolowski, Thomas R id: 3E999752-F248-11E8-B48F-1D18A9856A87 last_name: Sokolowski orcid: 0000-0002-1287-3779 - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 citation: ama: Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. Statistical analysis and optimality of neural systems. Neuron. 2021;109(7):1227-1241.e5. doi:10.1016/j.neuron.2021.01.020 apa: Mlynarski, W. F., Hledik, M., Sokolowski, T. R., & Tkačik, G. (2021). Statistical analysis and optimality of neural systems. Neuron. Cell Press. https://doi.org/10.1016/j.neuron.2021.01.020 chicago: Mlynarski, Wiktor F, Michal Hledik, Thomas R Sokolowski, and Gašper Tkačik. “Statistical Analysis and Optimality of Neural Systems.” Neuron. Cell Press, 2021. https://doi.org/10.1016/j.neuron.2021.01.020. ieee: W. F. Mlynarski, M. Hledik, T. R. Sokolowski, and G. Tkačik, “Statistical analysis and optimality of neural systems,” Neuron, vol. 109, no. 7. Cell Press, p. 1227–1241.e5, 2021. ista: Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. 2021. Statistical analysis and optimality of neural systems. Neuron. 109(7), 1227–1241.e5. mla: Mlynarski, Wiktor F., et al. “Statistical Analysis and Optimality of Neural Systems.” Neuron, vol. 109, no. 7, Cell Press, 2021, p. 1227–1241.e5, doi:10.1016/j.neuron.2021.01.020. short: W.F. Mlynarski, M. Hledik, T.R. Sokolowski, G. Tkačik, Neuron 109 (2021) 1227–1241.e5. date_created: 2020-02-28T11:00:12Z date_published: 2021-04-07T00:00:00Z date_updated: 2024-03-06T14:22:51Z day: '07' department: - _id: GaTk doi: 10.1016/j.neuron.2021.01.020 ec_funded: 1 external_id: isi: - '000637809600006' intvolume: ' 109' isi: 1 issue: '7' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1101/848374 month: '04' oa: 1 oa_version: Preprint page: 1227-1241.e5 project: - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships publication: Neuron publication_status: published publisher: Cell Press quality_controlled: '1' related_material: link: - description: News on IST Homepage relation: press_release url: https://ist.ac.at/en/news/can-evolution-be-predicted/ record: - id: '15020' relation: dissertation_contains status: public scopus_import: '1' status: public title: Statistical analysis and optimality of neural systems type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 109 year: '2021' ... --- _id: '7606' abstract: - lang: eng text: We derive a tight lower bound on equivocation (conditional entropy), or equivalently a tight upper bound on mutual information between a signal variable and channel outputs. The bound is in terms of the joint distribution of the signals and maximum a posteriori decodes (most probable signals given channel output). As part of our derivation, we describe the key properties of the distribution of signals, channel outputs and decodes, that minimizes equivocation and maximizes mutual information. This work addresses a problem in data analysis, where mutual information between signals and decodes is sometimes used to lower bound the mutual information between signals and channel outputs. Our result provides a corresponding upper bound. article_number: '8989292' article_processing_charge: No author: - first_name: Michal full_name: Hledik, Michal id: 4171253A-F248-11E8-B48F-1D18A9856A87 last_name: Hledik - first_name: Thomas R full_name: Sokolowski, Thomas R id: 3E999752-F248-11E8-B48F-1D18A9856A87 last_name: Sokolowski orcid: 0000-0002-1287-3779 - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 citation: ama: 'Hledik M, Sokolowski TR, Tkačik G. A tight upper bound on mutual information. In: IEEE Information Theory Workshop, ITW 2019. IEEE; 2019. doi:10.1109/ITW44776.2019.8989292' apa: 'Hledik, M., Sokolowski, T. R., & Tkačik, G. (2019). A tight upper bound on mutual information. In IEEE Information Theory Workshop, ITW 2019. Visby, Sweden: IEEE. https://doi.org/10.1109/ITW44776.2019.8989292' chicago: Hledik, Michal, Thomas R Sokolowski, and Gašper Tkačik. “A Tight Upper Bound on Mutual Information.” In IEEE Information Theory Workshop, ITW 2019. IEEE, 2019. https://doi.org/10.1109/ITW44776.2019.8989292. ieee: M. Hledik, T. R. Sokolowski, and G. Tkačik, “A tight upper bound on mutual information,” in IEEE Information Theory Workshop, ITW 2019, Visby, Sweden, 2019. ista: Hledik M, Sokolowski TR, Tkačik G. 2019. A tight upper bound on mutual information. IEEE Information Theory Workshop, ITW 2019. Information Theory Workshop, 8989292. mla: Hledik, Michal, et al. “A Tight Upper Bound on Mutual Information.” IEEE Information Theory Workshop, ITW 2019, 8989292, IEEE, 2019, doi:10.1109/ITW44776.2019.8989292. short: M. Hledik, T.R. Sokolowski, G. Tkačik, in:, IEEE Information Theory Workshop, ITW 2019, IEEE, 2019. conference: end_date: 2019-08-28 location: Visby, Sweden name: Information Theory Workshop start_date: 2019-08-25 date_created: 2020-03-22T23:00:47Z date_published: 2019-08-01T00:00:00Z date_updated: 2024-03-06T14:22:51Z day: '01' department: - _id: GaTk doi: 10.1109/ITW44776.2019.8989292 ec_funded: 1 external_id: arxiv: - '1812.01475' isi: - '000540384500015' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1812.01475 month: '08' oa: 1 oa_version: Preprint project: - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication: IEEE Information Theory Workshop, ITW 2019 publication_identifier: isbn: - '9781538669006' publication_status: published publisher: IEEE quality_controlled: '1' related_material: record: - id: '15020' relation: dissertation_contains status: public scopus_import: '1' status: public title: A tight upper bound on mutual information type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2019' ...